AI Support Engineer Lead (US)

1 мес. назад

USALeadOnsite

ai systemssupport

The AI Support Engineer Lead role involves both running complex support processes and building AI systems to reduce support workload.

Требования

Every candidate and employee's success is measured against the same 3 dimensions: Aptitude, Attitude and Agency.

Условия

Competitive compensation based on level and experience $240,000 - $350,000
Significant equity package at a Sequoia-backed startup
Health benefits for you and your dependents
New MacBook Pro or Linux machine, monitor, keyboard, etc.
Opportunity to travel between our Paris and San Francisco offices
Regular team events and off-sites

Другое

Work is being rewritten, and the people holding the pen are the ones who actually run it.
With enterprise-grade governance, flexible model choice, and a collaborative interface for humans and agents to work together, empowers AI Operators at the world’s fastest-moving companies to rewire how work gets done.
With 70%+ weekly active users, people stick with as much as they do with Slack and Notion. We don't get piloted and shelved. We land once, and spread. We're at an exciting stage of our journey, and growing fast.
We're serving great customers like Datadog, 1Password, Cursor, Clay, Vanta and Persona, and aim to x5 our growth by the end of 2026.
is backed by Sequoia with a determined team of optimists (coming from Stripe, OpenAI, and Stanford) who like to focus on users, ship fast, and don't take themselves too seriously while doing so. The Generalist named us among the Future 50 .
At , we're coining the term AI Operator : someone who rethinks and rebuilds company processes around AI. Not "how can AI help us do this faster?" but "if AI existed from day one, would we even do this the same way?"
The AI Support Engineer applies this mindset to Support. You'll split your time between running support (handling complex issues when agents fall short) and building the AI systems that reduce that workload over time. Support at is not a cost center, it's a product. Success is measured by eliminating categories of tickets, not just resolving them.
You will define, ship, and continuously iterate on the infrastructure that lets deliver a world-class support experience at scale: AI agents, automation workflows, classification systems, knowledge systems, and tooling built on top of itself. You will dogfood the product harder than almost anyone at the company.
We're hiring depending on your experience and technical depth. You'll be an experienced support engineer ready to build systems at scale, we want to hear from you.
Set the technical direction for the support stack: what gets automated, in what order, and to what standard. You make the calls, not just the builds.
Design, ship, and maintain AI agents and automation workflows that reduce manual support load: think ticket classification, acknowledgment automation, response drafting, incident detection, and proactive user outreach.
Identify recurring categories of issues and engineer them out of existence through automation, documentation, prompt iteration, or product feedback.
Build and maintain tooling (MCP integrations, agents, internal scripts) that increase the team's capacity without increasing headcount.
Define and own the "Support as a Product" backlog. You decide what goes on it, coach the team to execute it, and hold the bar on what shipped means.
Hire, onboard, and develop the support engineers around you. You're not just resolving tickets and building systems, you're growing the people who will.
Set the standard for how complex issues get handled at . You take the hardest tickets yourself. The team knows what good looks like because they've watched you do it.
Investigate complex issues across logs, code, and internal tooling to identify root causes and provide clear answers to customers.
Handle escalated cases with precise, accessible communication for both technical and non-technical audiences.
Systematically analyze agent-generated responses for inconsistencies and iterate on prompts, documentation, and tooling until human intervention is minimal.
Represent support at the engineering and product level. You synthesize signal, prioritize it, and push it through with enough context that it actually moves things, not just relays it.
Build strong working relationships with engineers and customer-facing teams to ensure efficient, high-context escalations.
Own the feedback loop end-to-end: when agents fail due to missing or incorrect information, close the gap across engineering, product, and documentation.
Function-building experience: You've defined or significantly shaped a support engineering function before: built the operating model, set the standards, hired into the team, or rebuilt a broken process from scratch.
High technical aptitude: comfortable reading code, analyzing logs, navigating codebases, and troubleshooting distributed systems. You're not stopped by a stack trace.
AI fluency at builder level: track record of building custom agents, automations, or workflows using AI tools (, Cursor, Claude Code, n8n, etc.); not just prompting.
Exceptional prioritization: You know what to solve immediately, what to automate, what to escalate, and, critically, what to delegate and to whom. You make these calls for yourself and for the team, fast and without second-guessing.
People development instincts: You've coached and grown engineers before, even informally. You know how to give feedback that sticks and create conditions where people do their best work.
Bidirectional communication: can translate technical concepts to non-technical audiences and speak technical language fluently with engineers.
Interest in or experience with a Build/Run split: Whether you've already operated this way or you're excited to learn this approach, you see support as more than ticket resolution.
Builder mentality: You instinctively automate repetitive work rather than accepting inefficient systems, and you hold your team to the same standard.
Resilience-empathy paradox: You can absorb customer frustration without taking it personally while still deeply caring about the user experience.
Leads by example: You're not above the queue. When something is hard, you take it.
Exceptional prioritization: You know what to solve immediately, what to automate, and what to escalate.
Thrives in ambiguity: You enjoy building systems from scratch in environments where many processes are still undefined.
Low ego, team player: You share knowledge freely and care about the success of the broader team.
High ownership: You define what needs to be done as often as you execute it. You don't wait for someone to tell you the function has a gap.
Systems-first thinking: You focus on eliminating categories of work, not simply resolving tickets faster. You hold yourself and the team accountable to that distinction.
Pattern recognition to repeatability: You turn recurring issues into scalable systems, automations, workflows, or documentation that eliminate future occurrences.
Deep investigation instincts: You investigate as far as possible before escalating, providing engineering teams with thoughtful, high-context information when collaboration is needed, and you coach the team to do the same.
Even if you don’t meet every requirement above, we still encourage you to apply. We care deeply about curiosity, potential, and determination, and we know exceptional people don’t always fit neatly into a checklist.
We're prioritizing building our team with an in-person culture at our offices in Paris, San Francisco, and New York because we value the magic that happens when talented people work closely together.
We have an office-first culture. Some of the best things about building at are the energy, the fast decisions, and the unexpected conversations that unlock a hard problem, which happen because we are in the same room. Being together is not a formality, it is how we do our best work, and it is something we actively protect. That said, we hire people with strong judgement and we extend that trust to how they manage their time. When working from home makes more sense for what you need to get done that day, we trust you to make that call.
The models are powerful enough. What's missing is the product layer where AI meets how companies actually work. That's what we're building: the infrastructure that lets any team turn scattered knowledge and tools into coordinated execution with agents they build, own, and run themselves.
We use ourselves every day. We get to shape how humans and agents collaborate while solving our own problems with the product we ship. That loop is rare, and it's why we move fast.
If you're excited about defining a new category and want to join a determined team of optimists who focus on users, ship fast, and don't take themselves too seriously, we'd love to talk.
Even if you don't check every box in our requirements, we encourage you to apply. We value diverse perspectives and backgrounds, and we're more interested in your potential and passion than a perfect match to our checklist.
Our product constitution, a story about our mission
Agents at work - Latent Space , podcast with our cofounder, Stanislas Polu, 2024
LLMs reasoning and agentic capabilities over time - dotAI, podcast with our cofounder, Stanislas Polu, 2024